AWS sagemaker documentation change
Summary
Updated documentation links by adding double slashes in URLs to maintain consistent formatting across AWS documentation references.
Security assessment
The changes only modify URL formatting (adding redundant slashes) without altering security-related content. No security vulnerabilities, access control changes, or data protection updates are mentioned in the diff. The updates to IAM role references and S3 bucket guidance maintain existing security recommendations without introducing new security context.
Diff
diff --git a/sagemaker/latest/dg/nova-fine-tuning-training-job.md b/sagemaker/latest/dg/nova-fine-tuning-training-job.md index 0267fb8b9..6af80fe90 100644 --- a//sagemaker/latest/dg/nova-fine-tuning-training-job.md +++ b//sagemaker/latest/dg/nova-fine-tuning-training-job.md @@ -30 +30 @@ Before you start a training job, note the following. - * Amazon S3 buckets to store your input data and output of training jobs. You can either use one bucket for both or separate buckets for each type of the data. Make sure your buckets are in the same AWS Region where you create all the other resources for training. For more information, see [Creating a general purpose bucket](https://docs.aws.amazon.com/AmazonS3/latest/userguide/create-bucket-overview.html). + * Amazon S3 buckets to store your input data and output of training jobs. You can either use one bucket for both or separate buckets for each type of the data. Make sure your buckets are in the same AWS Region where you create all the other resources for training. For more information, see [Creating a general purpose bucket](https://docs.aws.amazon.com//AmazonS3/latest/userguide/create-bucket-overview.html). @@ -32 +32 @@ Before you start a training job, note the following. - * An IAM role with permissions to run a training job. Make sure you attach an IAM policy with `AmazonSageMakerFullAccess`. For more information, see [How to use SageMaker execution roles](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html). + * An IAM role with permissions to run a training job. Make sure you attach an IAM policy with `AmazonSageMakerFullAccess`. For more information, see [How to use SageMaker execution roles](https://docs.aws.amazon.com//sagemaker/latest/dg/sagemaker-roles.html). @@ -45 +45 @@ Preparing high-quality, properly formatted data is a critical first step in the -SFT data format requirements - For both full-rank SFT and LoRA SFT, data should follow the Converse format. For examples and constraints of this format, see [Preparing data for fine-tuning Understanding models](https://docs.aws.amazon.com/nova/latest/userguide/fine-tune-prepare-data-understanding.html). +SFT data format requirements - For both full-rank SFT and LoRA SFT, data should follow the Converse format. For examples and constraints of this format, see [Preparing data for fine-tuning Understanding models](https://docs.aws.amazon.com//nova/latest/userguide/fine-tune-prepare-data-understanding.html). @@ -53 +53 @@ DPO data format requirements - For both DPO in full-rank and DPO with LoRA, data -DPO dataset other constraints - Other constraints on datasets are the same for SFT. For more information, see [Dataset constraints](https://docs.aws.amazon.com/nova/latest/userguide/fine-tune-prepare-data-understanding.html). A single JSONL file for training and a single JSONL file for validation is expected. Validation set is optional. +DPO dataset other constraints - Other constraints on datasets are the same for SFT. For more information, see [Dataset constraints](https://docs.aws.amazon.com//nova/latest/userguide/fine-tune-prepare-data-understanding.html). A single JSONL file for training and a single JSONL file for validation is expected. Validation set is optional. @@ -229 +229 @@ DPO dataset recommendations - A minimum of 1,000 preference pairs for effective -Training jobs default to a 1-day time limit, though the estimates in the tables below assume a 5-day training duration for illustration purposes. As a best practice, we recommend increasing your training time limit to 28 days maximum to accommodate longer training workloads. To request a limit increase, see [Requesting a quota increase](https://docs.aws.amazon.com/servicequotas/latest/userguide/request-quota-increase.html). +Training jobs default to a 1-day time limit, though the estimates in the tables below assume a 5-day training duration for illustration purposes. As a best practice, we recommend increasing your training time limit to 28 days maximum to accommodate longer training workloads. To request a limit increase, see [Requesting a quota increase](https://docs.aws.amazon.com//servicequotas/latest/userguide/request-quota-increase.html). @@ -280 +280 @@ By reducing the number of epochs or the context length of your records, you coul -This section covers guidance on recipe configurations for both full-rank supervised fine-tuning (SFT) and low-rank adaptation parameter-efficient fine-tuning (LoRA PEFT) approaches. These recipe files serve as the blueprint for your model customization jobs, allowing you to specify training parameters, hyperparameters, and other critical settings that determine how your model learns from your data. To adjust the hyperparameters, follow the guidelines in [Selecting hyperparameters](https://docs.aws.amazon.com/nova/latest/userguide/customize-fine-tune-hyperparameters.html). +This section covers guidance on recipe configurations for both full-rank supervised fine-tuning (SFT) and low-rank adaptation parameter-efficient fine-tuning (LoRA PEFT) approaches. These recipe files serve as the blueprint for your model customization jobs, allowing you to specify training parameters, hyperparameters, and other critical settings that determine how your model learns from your data. To adjust the hyperparameters, follow the guidelines in [Selecting hyperparameters](https://docs.aws.amazon.com//nova/latest/userguide/customize-fine-tune-hyperparameters.html). @@ -347 +347 @@ model.peft | lora_tuning.adapter_dropout | Regularization for LoRA parameters. -This section demonstrates how to run a customized Nova model on SageMaker training jobs through a Jupyter notebook environment. You'll find a complete example that walks through the process of configuring and launching a training job, along with reference tables for selecting the appropriate container image URIs and instance configurations. This approach gives you programmatic control over your fine-tuning workflows while leveraging SageMaker's managed infrastructure for model customization. For more information, see [Use a SageMaker AI estimator to run a training job](https://docs.aws.amazon.com/sagemaker/latest/dg/docker-containers-adapt-your-own-private-registry-estimator.html). +This section demonstrates how to run a customized Nova model on SageMaker training jobs through a Jupyter notebook environment. You'll find a complete example that walks through the process of configuring and launching a training job, along with reference tables for selecting the appropriate container image URIs and instance configurations. This approach gives you programmatic control over your fine-tuning workflows while leveraging SageMaker's managed infrastructure for model customization. For more information, see [Use a SageMaker AI estimator to run a training job](https://docs.aws.amazon.com//sagemaker/latest/dg/docker-containers-adapt-your-own-private-registry-estimator.html). @@ -377 +377 @@ Amazon Nova Pro | Fine-tuning (DPO) | `p5.48xlarge, p5en.48xlarge` | 6 | 6,12,2 -The following sample notebook demonstrates how to run a training job. For additional getting started notebooks on how to customize Nova models using SageMaker AI training jobs, see [Use a SageMaker AI estimator to run a training job](https://docs.aws.amazon.com/sagemaker/latest/dg/docker-containers-adapt-your-own-private-registry-estimator.html). +The following sample notebook demonstrates how to run a training job. For additional getting started notebooks on how to customize Nova models using SageMaker AI training jobs, see [Use a SageMaker AI estimator to run a training job](https://docs.aws.amazon.com//sagemaker/latest/dg/docker-containers-adapt-your-own-private-registry-estimator.html). @@ -458 +458 @@ The following sample notebook demonstrates how to run a training job. For additi -Fine-tuning your Nova LLM model effectively requires careful selection of hyperparameters. While this section explains the basic recipe structure and components, optimizing hyperparameters for your specific use case often requires additional guidance. For comprehensive recommendations on hyperparameter selection, best practices, and optimization strategies, see [Selecting hyperparameters](https://docs.aws.amazon.com/nova/latest/userguide/customize-fine-tune-hyperparameters.html). This resource provides detailed guidance on selecting appropriate learning rates, batch sizes, training epochs, and other critical parameters based on your dataset characteristics and training objectives. We recommend consulting this guide when fine-tuning your recipe configuration to achieve optimal model performance. +Fine-tuning your Nova LLM model effectively requires careful selection of hyperparameters. While this section explains the basic recipe structure and components, optimizing hyperparameters for your specific use case often requires additional guidance. For comprehensive recommendations on hyperparameter selection, best practices, and optimization strategies, see [Selecting hyperparameters](https://docs.aws.amazon.com//nova/latest/userguide/customize-fine-tune-hyperparameters.html). This resource provides detailed guidance on selecting appropriate learning rates, batch sizes, training epochs, and other critical parameters based on your dataset characteristics and training objectives. We recommend consulting this guide when fine-tuning your recipe configuration to achieve optimal model performance. @@ -460 +460 @@ Fine-tuning your Nova LLM model effectively requires careful selection of hyperp -For details about minimum, maximum, and default values for epochs, learning rate, and learning warmup steps, see [Hyperparameters for Understanding models](https://docs.aws.amazon.com/nova/latest/userguide/fine-tune-hyperparameters-understanding-models.html). +For details about minimum, maximum, and default values for epochs, learning rate, and learning warmup steps, see [Hyperparameters for Understanding models](https://docs.aws.amazon.com//nova/latest/userguide/fine-tune-hyperparameters-understanding-models.html).